In the end, it is reliability that matters most.
It's the flavoring that give your music and artistry it's reliability.
Therefore, it is simpler to express its reliability as R(S series) = Prob(π = {π i = 1, ∀i = 1 , … , n}) ( ={prod}_{i=1}^n{R}_i ), where R i is the reliability of the i th component during the system's mission time.
RI(i) is the reliability index of the prediction result of RNA-binding residue i obtained from PRBR.
RI(i) is the reliability index of the prediction result of nonbinding residue i obtained from PRBR.
R is a diagonal matrix with elements R ii = (1/ rel i )-1, where rel i is the reliability of the DTD of ith bull.
where (I^{B}_{i}) is the reliability importance of component i, p i is the component reliability vector with the ith component removed, 0 i represents the condition when component i fails, and 1 i describes the condition when i is working.
Furthermore, Olsson and Schubert (2007) observe that, while coherence falls short of being truth conducive, it can still be "reliability conducive", i.e., more coherence, according to some measures, entails a higher probability that the sources are reliable, at least in a paradigmatic case (cf. Schubert 2012a, 2011).
where P ^ i is the relative reliability of peer i as defined in §4.2 and Cov[X, Y] denotes the covariance of random variables X and Y.
Then the reliability of an interaction (a, b) is estimated as: r e l i a b i l i t y (a, b ) = 1 - ∏ i ∈ E a, b (1 - r e l i ) n i, a, b where rel i is the estimated reliability of experimental method i, E a,b is the set of experimental methods that detected interaction (a, b), and n i,a,b is the number of times that experimental method i detected interaction (a, b).
